Abstract
With the rapid development of computer big data technology, online education in the form of online courses is increasingly becoming an important means of education. In order to objectively evaluate the teaching quality of online classroom, a teaching quality evaluation system based on facial feature recognition is proposed. The improved (MTCNN) multitask convolutional neural network is used to determine the face region, and then the eye and mouth regions are located according to the facial proportion relationship of the face. The light AlexNet classification based on Ghost module was used to detect the open and close state of eyes and mouth and combined with PERCLOS (percentage of eye closure) index values to achieve fatigue detection. Large range pose estimation from pitch, yaw, and roll angles can be achieved by easily locating facial feature angles. Finally, the fuzzy comprehensive evaluation method is used to evaluate students’ learning concentration. The simulation experiments are conducted, and the results show that the proposed system can objectively evaluate the teaching quality of online courses according to students' facial feature recognition.
Highlights
Online education, as a brand new education model, has been gradually welcomed by students and parents due to its characteristics of openness and diversity [1]
Scientific Programming students, the exploration of online classroom teaching quality evaluation system has become an important task of online education development
In order to objectively evaluate the teaching quality of online classroom, a teaching quality evaluation system based on facial feature recognition is proposed. e effectiveness of the scheme needs to be tested in practical application. erefore, a detection scheme is designed in this paper. e subjects were watching a 40-minute teaching video on the computer while the detection system started working
Summary
As a brand new education model, has been gradually welcomed by students and parents due to its characteristics of openness and diversity [1]. Scientific Programming students, the exploration of online classroom teaching quality evaluation system has become an important task of online education development. Online education has the characteristics of loose structure and open distance teaching environment, in which online learning effect evaluation is an important part. Literature [7] discusses how to improve the learning effect and efficiency of learners through positive emotion and positive emotion in online education. Literature [9] builds an emotional interaction model of social learning network, which can identify learners’ emotional states in online education. Is paper presents an online classroom teaching quality evaluation system based on facial feature recognition. E simulation results show that the system can effectively evaluate students’ concentration in class according to the results of face detection, so as to evaluate the teaching quality of online education.
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